Predication of entropy generation rate in a concentrating photovoltaic thermal system with twisted tube turbulator using Boosted regression tree algorithm

Efficient energy conversion and utilization remain paramount in addressing the growing energy demand and environmental concerns. Concentrating photovoltaic thermal (CPVT) systems have emerged as promising solutions by integrating photovoltaic (PV) cells with thermal components for simultaneous electricity and heat generation. In this paper, we propose the application of the Boosted Regression Tree (BRT) algorithm to predict the entropy generation rate in a CPVT system equipped with a perforated twisted tube turbulator. Brief introduction of numerical analysis of local and global rates of frictional ((S)over dot(fr)) and thermal ((S)over dot(th)) irreversibilities in a CPVT system equipped with a perforated twisted tube turbulator. The results approve the efficacy of the BRT algorithm in predicting the entropy generation rate. Through comprehensive simulations and data analysis, we establish a predictive model that considers factors such as solar irradiance, fluid flow rate, tube geometry, and turbulator characteristics. The BRT model exhibits remarkable accuracy in capturing the nuanced interplay of these factors, enabling reliable estimations of entropy generation rate.